Tim van Erven

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Wouter M. KoolenCentrum Wiskunde & Informatica; University of TwenteVerified email at cwi.nl
Peter GrünwaldGroup Leader Machine Learning, CWI; Professor of Statistics, Leiden UniversityVerified email at cwi.nl
Wojciech KotłowskiPoznan University of TechnologyVerified email at cs.put.poznan.pl
Mark D. ReidAppleVerified email at reid.name
Robert C. WilliamsonUniversity of TübingenVerified email at uni-tuebingen.de
Dirk van der HoevenUniversity of AmsterdamVerified email at dirkvanderhoeven.com
Jairo CugliariUniversité Lumière Lyon 2Verified email at univ-lyon2.fr
Pierre GaillardINRIAVerified email at gaillard.me
Peter HarremoesLektor i matematik, Niels BrockVerified email at niels.brock.dk
Hedi HADIJICentraleSupelecVerified email at centralesupelec.fr
Nishant A. MehtaAssistant Professor, University of VictoriaVerified email at uvic.ca
Manfred K. Warmuthvisiting Google BrainVerified email at google.com
Zakaria MhammediMassachusetts Institute of TechnologyVerified email at mit.edu
Hidde FokkemaPhD student, University of AmsterdamVerified email at uva.nl
Cristobal GuzmanPontificia Universidad Católica de ChileVerified email at mat.uc.cl
Jack J. MayoKorteweg-de Vries Institute for Mathematics, University of AmsterdamVerified email at uva.nl
Botond SzabóAssociate Professor, Bocconi UniversityVerified email at unibocconi.it
Vlado MenkovskiEindhoven University of TechnologyVerified email at tue.nl
Henry BrightonTilburg UniversityVerified email at tilburguniversity.edu
Rianne de HeideAssistant professor, Mathematics department, Vrije Universiteit AmsterdamVerified email at vu.nl

Tim van Erven

Associate professor at the University of Amsterdam, the Netherlands

Verified email at uva.nl - Homepage

Online Learning Statistical Learning Theory Explainable Machine Learning


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R\'enyi Divergence and Kullback-Leibler Divergence T Van Erven, P Harremoës IEEE Transactions on Information Theory 60 (7), 3797-3820, 2014	1399	2014
Follow the leader if you can, hedge if you must S de Rooij, T Van Erven, PD Grünwald, WM Koolen Journal of Machine Learning Research 15, 1281-1316, 2014	200	2014
A second-order bound with excess losses P Gaillard, G Stoltz, T Van Erven Conference on Learning Theory, 176-196, 2014	147	2014
Second-order quantile methods for experts and combinatorial games WM Koolen, T Van Erven Conference on Learning Theory, 1155-1175, 2015	112	2015
Catching up faster by switching sooner: A predictive approach to adaptive estimation with an application to the AIC–BIC dilemma T Van Erven, P Grünwald, S De Rooij Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2012	112*	2012
Fast rates in statistical and online learning T Van Erven, P Grunwald, NA Mehta, M Reid, R Williamson MIT Press, 2015	104	2015
Metagrad: Multiple learning rates in online learning T Van Erven, WM Koolen Advances in Neural Information Processing Systems 29, 2016	89	2016
Game-theoretically optimal reconciliation of contemporaneous hierarchical time series forecasts T Van Erven, J Cugliari Modeling and stochastic learning for forecasting in high dimensions, 297-317, 2015	73	2015
Rényi divergence and majorization T van Erven, P Harremoës 2010 IEEE International Symposium on Information Theory, 1335-1339, 2010	64	2010
Follow the leader with dropout perturbations T Van Erven, W Kotłowski, MK Warmuth Conference on Learning Theory, 949-974, 2014	55	2014
Adaptive hedge T Erven, WM Koolen, S Rooij, P Grünwald Advances in Neural Information Processing Systems 24, 2011	54	2011
Catching up faster in Bayesian model selection and model averaging T Erven, S Rooij, P Grünwald Advances in Neural Information Processing Systems 20, 2007	51	2007
Combining adversarial guarantees and stochastic fast rates in online learning WM Koolen, P Grünwald, T Van Erven Advances in Neural Information Processing Systems 29, 2016	47	2016
The many faces of exponential weights in online learning D Hoeven, T Erven, W Kotłowski Conference On Learning Theory, 2067-2092, 2018	34	2018
Learning the learning rate for prediction with expert advice WM Koolen, T Van Erven, P Grünwald Advances in neural information processing systems 27, 2014	32	2014
Mixability is Bayes Risk Curvature Relative to Log Loss T van Erven, MD Reid, RC Williamson The Journal of Machine Learning Research, 1639-1663, 2012	26*	2012
PAC-Bayes mini-tutorial: A continuous union bound T van Erven arXiv preprint arXiv:1405.1580, 2014	25	2014
Lipschitz adaptivity with multiple learning rates in online learning Z Mhammedi, WM Koolen, T Van Erven Conference on Learning Theory, 2490-2511, 2019	21	2019
Learning the switching rate by discretising Bernoulli sources online S Rooij, T Erven Artificial Intelligence and Statistics, 432-439, 2009	20	2009
Mixability in statistical learning T Erven, P Grünwald, MD Reid, RC Williamson Advances in Neural Information Processing Systems 25, 2012	17	2012

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